<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Pro Trailblazer</title><description>Guides, products, and recommended solutions from Pro Trailblazer. Built for people who need to move fast and build well.</description><link>https://protrailblazer.com/</link><language>en-us</language><item><title>36 ways AI works alongside a freelance web developer</title><link>https://protrailblazer.com/posts/freelancer-ai-workflow/</link><guid isPermaLink="true">https://protrailblazer.com/posts/freelancer-ai-workflow/</guid><description>A 3D tour of six freelance web project phases with six AI assists each, plus a toggle showing which 16 of 36 handoffs can drop the human approver.</description><pubDate>Thu, 07 May 2026 00:00:00 GMT</pubDate></item><item><title>Agentic workflows: how AI agents chain business tasks</title><link>https://protrailblazer.com/posts/agentic-workflow-smb-guide/</link><guid isPermaLink="true">https://protrailblazer.com/posts/agentic-workflow-smb-guide/</guid><description>A radial simulation of an agentic AI workflow, plus a plain-language look at what agents do, why the orchestrator matters, and what breaks first for SMBs.</description><pubDate>Fri, 24 Apr 2026 00:00:00 GMT</pubDate></item><item><title>LLM hallucination: why confident answers drift from truth</title><link>https://protrailblazer.com/posts/hallucination-simulation/</link><guid isPermaLink="true">https://protrailblazer.com/posts/hallucination-simulation/</guid><description>A 3D demo of language-model sampling: tokens drift outward as text moves from grounded to fabricated, while the model&apos;s confidence barely moves.</description><pubDate>Fri, 24 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Model Context Protocol: how AI apps plug into tools and data</title><link>https://protrailblazer.com/posts/model-context-protocol-explained/</link><guid isPermaLink="true">https://protrailblazer.com/posts/model-context-protocol-explained/</guid><description>Watch JSON-RPC messages flow between an MCP host and its servers as the client discovers tools, invokes them, and streams results back to the model.</description><pubDate>Fri, 24 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Prompt engineering: how technique shapes what models say</title><link>https://protrailblazer.com/posts/prompt-engineering-guide/</link><guid isPermaLink="true">https://protrailblazer.com/posts/prompt-engineering-guide/</guid><description>Zero-shot, few-shot, chain-of-thought, and role prompting explained with an interactive demo. See how technique shifts token probability and output quality.</description><pubDate>Fri, 24 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Retrieval-augmented generation: how LLMs look things up</title><link>https://protrailblazer.com/posts/retrieval-augmented-generation/</link><guid isPermaLink="true">https://protrailblazer.com/posts/retrieval-augmented-generation/</guid><description>A 3D demo of a RAG pipeline: a query vector searches a corpus, the top-K nearest chunks light up, and the LLM grounds its answer in the retrieved text.</description><pubDate>Fri, 24 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Autoregressive generation: how an LLM writes one token at a time</title><link>https://protrailblazer.com/posts/how-llms-generate-text/</link><guid isPermaLink="true">https://protrailblazer.com/posts/how-llms-generate-text/</guid><description>A plain-English look at how LLMs actually produce text, with a 3D demo of the KV cache that makes inference feasible and what would happen without it.</description><pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Inference-time compute: more samples, smarter answers</title><link>https://protrailblazer.com/posts/inference-time-compute-explained/</link><guid isPermaLink="true">https://protrailblazer.com/posts/inference-time-compute-explained/</guid><description>A plain-English look at inference-time compute, with a live grid of reasoning chains that vote on the same question and surface the real accuracy-vs-N curve.</description><pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate></item><item><title>LLM as judge: one model grading another</title><link>https://protrailblazer.com/posts/llm-as-judge-explained/</link><guid isPermaLink="true">https://protrailblazer.com/posts/llm-as-judge-explained/</guid><description>One LLM grades another: a live demo of pairwise, rubric, and best-of-N judging, plus the position, length, and self-preference biases you have to probe for.</description><pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate></item><item><title>LLM temperature: one number that reshapes the next-token distribution</title><link>https://protrailblazer.com/posts/llm-temperature-explained/</link><guid isPermaLink="true">https://protrailblazer.com/posts/llm-temperature-explained/</guid><description>A plain-English look at temperature in language model inference, with a live demo that shows exactly what the parameter does to the probability distribution.</description><pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Mixture of Experts: sparse routing for huge models at fast-model cost</title><link>https://protrailblazer.com/posts/mixture-of-experts-explained/</link><guid isPermaLink="true">https://protrailblazer.com/posts/mixture-of-experts-explained/</guid><description>A plain-English explainer on MoE models with a live 3D demo showing how tokens route through a small subset of experts at each layer.</description><pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Top-k and top-p sampling: how an LLM picks its next token</title><link>https://protrailblazer.com/posts/top-k-top-p-sampling-explained/</link><guid isPermaLink="true">https://protrailblazer.com/posts/top-k-top-p-sampling-explained/</guid><description>A plain-English look at the two cutoff strategies that decide which tokens a language model is allowed to sample from, with a live interactive demo.</description><pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Gradient descent: how a model rolls downhill</title><link>https://protrailblazer.com/posts/gradient-descent-demo/</link><guid isPermaLink="true">https://protrailblazer.com/posts/gradient-descent-demo/</guid><description>Watch a simulated model find a low spot on a 3D loss surface. A plain-English look at the optimizer that drives modern machine learning.</description><pubDate>Sat, 18 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Embeddings in 3D: how models turn words into coordinates</title><link>https://protrailblazer.com/posts/embeddings-in-3d/</link><guid isPermaLink="true">https://protrailblazer.com/posts/embeddings-in-3d/</guid><description>Type a sentence, watch each token land as a point in 3D space. Click any token to inspect its vector and see which other tokens it&apos;s nearest to.</description><pubDate>Sat, 18 Apr 2026 00:00:00 GMT</pubDate></item></channel></rss>